Effects of region of interest selection on phase history based SAR moving target autofocus

2019 ◽  
Author(s):  
David A. Garren
Keyword(s):  
Region Of Interest ◽  
Moving Target

scholarly journals Recognition of Student Classroom Behaviors Based on Moving Target Detection

2021 ◽  
Vol 38 (1) ◽  
pp. 215-220
Author(s):  
Bin Wu ◽  
Chunmei Wang ◽  
Wei Huang ◽  
Da Huang ◽  
Hang Peng
Keyword(s):  
Target Detection ◽  
Classroom Environment ◽  
Skin Color ◽  
Student Behavior ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Moving Target ◽  
Classroom Behaviors ◽  
Moving Target Detection

Classroom teaching, as the basic form of teaching, provides students with an important channel to acquire information and skills. The academic performance of students can be evaluated and predicted objectively based on the data on their classroom behaviors. Considering the complexity of classroom environment, this paper firstly envisages a moving target detection algorithm for student behavior recognition in class. Based on region of interest (ROI) and face tracking, the authors proposed two algorithms to recognize the standing behavior of students in class. Moreover, a recognition algorithm was developed for hand raising in class based on skin color detection. Through experiments, the proposed algorithms were proved as effective in recognition of student classroom behaviors.

scholarly journals Tiny Drone Tracking Framework Using Multiple Trackers and Kalman-based Predictor

2021 ◽  
Author(s):  
Sohee Son ◽  
Jeongin Kwon ◽  
Hui-Yong Kim ◽  
Haechul Choi
Keyword(s):  
Region Of Interest ◽  
Security And Privacy ◽  
Moving Target ◽  
Moving Camera ◽  
Illumination Changes ◽  
Great Progress ◽  
Refinement Process ◽  
Average Success Rate ◽  
Privacy Issues ◽  
Motion Flow

Unmanned aerial vehicles like drones are one of the key development technologies with many beneficial applications. As they have made great progress, security and privacy issues are also growing. Drone tacking with a moving camera is one of the important methods to solve these issues. There are various challenges of drone tracking. First, drones move quickly and are usually tiny. Second, images captured by a moving camera have illumination changes. Moreover, the tracking should be performed in real-time for surveillance applications. For fast and accurate drone tracking, this paper proposes a tracking framework utilizing two trackers, a predictor, and a refinement process. One tracker finds a moving target based on motion flow and the other tracker locates the region of interest (ROI) employing histogram features. The predictor estimates the trajectory of the target by using a Kalman filter. The predictor contributes to keeping track of the target even if the trackers fail. Lastly, the refinement process decides the location of the target taking advantage of ROIs from the trackers and the predictor. In experiments on our dataset containing tiny flying drones, the proposed method achieved an average success rate of 1.134 times higher than conventional tracking methods and it performed at an average run-time of 21.08 frames per second.

scholarly journals Adaptive Colour Feature Identification in Image for Object Tracking

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Feng Su ◽  
Gu Fang ◽  
Ngai Ming Kwok
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Region Of Interest ◽  
Image Sequences ◽  
Moving Object ◽  
Moving Target ◽  
Feature Identification ◽  
Filtering Method ◽  
Colour Feature ◽  
Crowded Environment

Identification and tracking of a moving object using computer vision techniques is important in robotic surveillance. In this paper, an adaptive colour filtering method is introduced for identifying and tracking a moving object appearing in image sequences. This filter is capable of automatically identifying the most salient colour feature of the moving object in the image and using this for a robot to track the object. The method enables the selected colour feature to adapt to surrounding condition when it is changed. A method of determining the region of interest of the moving target is also developed for the adaptive colour filter to extract colour information. Experimental results show that by using a camera mounted on a robot, the proposed methods can perform robustly in tracking a randomly moving object using adaptively selected colour features in a crowded environment.

A technique for protecting delicate specimens during processing

1989 ◽  
Vol 47 ◽  
pp. 982-983
Author(s):  
R.J. Mount ◽  
R.V. Harrison
Keyword(s):  
Basilar Membrane ◽  
Low Cost ◽  
Organ Of Corti ◽  
Region Of Interest ◽  
Sensory Epithelium ◽  
Physical Damage ◽  
Air Drying ◽  
Specimen Handling ◽  
Two Component

The sensory end organ of the ear, the organ of Corti, rests on a thin basilar membrane which lies between the bone of the central modiolus and the bony wall of the cochlea. In vivo, the organ of Corti is protected by the bony wall which totally surrounds it. In order to examine the sensory epithelium by scanning electron microscopy it is necessary to dissect away the protective bone and expose the region of interest (Fig. 1). This leaves the fragile organ of Corti susceptible to physical damage during subsequent handling. In our laboratory cochlear specimens, after dissection, are routinely prepared by the O-T- O-T-O technique, critical point dried and then lightly sputter coated with gold. This processing involves considerable specimen handling including several hours on a rotator during which the organ of Corti is at risk of being physically damaged. The following procedure uses low cost, readily available materials to hold the specimen during processing ,preventing physical damage while allowing an unhindered exchange of fluids.Following fixation, the cochlea is dehydrated to 70% ethanol then dissected under ethanol to prevent air drying. The holder is prepared by punching a hole in the flexible snap cap of a Wheaton vial with a paper hole punch. A small amount of two component epoxy putty is well mixed then pushed through the hole in the cap. The putty on the inner cap is formed into a “cup” to hold the specimen (Fig. 2), the putty on the outside is smoothed into a “button” to give good attachment even when the cap is flexed during handling (Fig. 3). The cap is submerged in the 70% ethanol, the bone at the base of the cochlea is seated into the cup and the sides of the cup squeezed with forceps to grip it (Fig.4). Several types of epoxy putty have been tried, most are either soluble in ethanol to some degree or do not set in ethanol. The only putty we find successful is “DUROtm MASTERMENDtm Epoxy Extra Strength Ribbon” (Loctite Corp., Cleveland, Ohio), this is a blue and yellow ribbon which is kneaded to form a green putty, it is available at many hardware stores.

Where's That Wascally Wilber? The Challenges of Hitting a Moving Target

PsycCRITIQUES ◽  
2007 ◽  
Vol 52 (13) ◽  
Author(s):  
Douglas A. MacDonald
Keyword(s):  
Moving Target

Pediatric rehabilitation psychology: Rehabilitating a moving target.

2017 ◽  
Vol 62 (3) ◽  
pp. 223-226 ◽  
Author(s):  
Jacqueline N. Kaufman ◽  
Sarah Lahey ◽  
Beth S. Slomine
Keyword(s):  
Moving Target ◽  
Pediatric Rehabilitation ◽  
Rehabilitation Psychology

Quantitative accuracy of serotonergic neurotrans-mission imaging with high-resolution 123I SPECT

2004 ◽  
Vol 43 (06) ◽  
pp. 185-189 ◽  
Author(s):  
J. T. Kuikka
Keyword(s):  
High Resolution ◽  
Scatter Correction ◽  
Region Of Interest ◽  
Binding Potential ◽  
Partial Volume ◽  
Therapeutic Drugs ◽  
Quantitative Accuracy ◽  
Critical Error ◽  
Statistical Noise

Summary Aim: Serotonin transporter (SERT) imaging can be used to study the role of regional abnormalities of neurotransmitter release in various mental disorders and to study the mechanism of action of therapeutic drugs or drugs’ abuse. We examine the quantitative accuracy and reproducibility that can be achieved with high-resolution SPECT of serotonergic neurotransmission. Method: Binding potential (BP) of 123I labeled tracer specific for midbrain SERT was assessed in 20 healthy persons. The effects of scatter, attenuation, partial volume, mis-registration and statistical noise were estimated using phantom and human studies. Results: Without any correction, BP was underestimated by 73%. The partial volume error was the major component in this underestimation whereas the most critical error for the reproducibility was misplacement of region of interest (ROI). Conclusion: The proper ROI registration, the use of the multiple head gamma camera with transmission based scatter correction introduce more relevant results. However, due to the small dimensions of the midbrain SERT structures and poor spatial resolution of SPECT, the improvement without the partial volume correction is not great enough to restore the estimate of BP to that of the true one.

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